Essays about: "thesis E-commerce model"

Showing result 1 - 5 of 106 essays containing the words thesis E-commerce model.

  1. 1. Computationally Efficient Explainable AI: Bayesian Optimization for Computing Multiple Counterfactual Explanantions

    University essay from KTH/Matematik (Avd.)

    Author : Giorgio Sacchi; [2023]
    Keywords : Explainable AI; Counterfactual Explanations CFEs ; Bayesian Optimization BO ; Black-Box Models; Model-Agnostic; Machine Learning ML ; Efficient Computation; High-Stake Decisions; Förklarbar AI; Kontrafaktuell Förklaring CFE ; Bayesiansk Optimering BO ; Svarta lådmodeller; Modellagnostisk; Maskininlärning; Beräkningsmässigt Effektiv; Beslut med höga insatser;

    Abstract : In recent years, advanced machine learning (ML) models have revolutionized industries ranging from the healthcare sector to retail and E-commerce. However, these models have become increasingly complex, making it difficult for even domain experts to understand and retrace the model's decision-making process. READ MORE

  3. 2. Customer churn prediction in a slow fashion e-commerce context : An analysis of the effect of static data in customer churn prediction

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Luca Colasanti; [2023]
    Keywords : Survival Analysis; Time To Event prediction; Churn retention; Machine Learning; Deep Learning; Customer Clustering; E-commerce; Analisi di sopravvivenza; Previsione del tempo a evento; Ritenzione dall’abbandono dei clienti; Apprendimento automatico; Apprendimento profondo; Segmentazione della clientela; Commercio elettronico; Överlevnadsanalys; Tid till händelseförutsägelse; Churn Prediction; Maskininlärning; Djuplärning; Kundkluster; E-handel;

    Abstract : Survival analysis is a subfield of statistics where the goal is to analyse and model the data where the outcome is the time until the occurrence of an event of interest. Because of the intrinsic temporal nature of the analysis, the employment of more recently developed sequential models (Recurrent Neural Network (RNN) and Long Short Term Memory (LSTM)) has been paired with the use of dynamic temporal features, in contrast with the past reliance on static ones. READ MORE

  4. 3. Exploring the use of mobile warehouses in midsized urban and rural regions for last-mile transportation

    University essay from Högskolan Dalarna/Institutionen för information och teknik

    Author : Mfon Etuk; Radu Ștefan Mihai; [2023]
    Keywords : Mobile Warehouse; Last-Mile Logistics; Supply Chain; Logistics; Distribution;

    Abstract : Abstract: Efficient last-mile transportation is a continuous difficulty for businesses, particularly in e-commerce, necessitating the development of creative solutions. This thesis investigates the use of mobile warehouses in mid-sized urban and rural areas to help with this problem. READ MORE

  5. 4. Exploring the Use of Attention for Generation Z Fashion Style Recognition with User Annotations as Labels

    University essay from KTH/Skolan för elektroteknik och datavetenskap (EECS)

    Author : Niki Samakovlis; [2023]
    Keywords : Attention mechanism; CNN; Deep Learning; Fashion Style Recognition; Feature Extraction; Generation Z; Uppmärksamhetsmekanism; Faltningsnätverk; Djupinlärning; Igenkänning av klädstilar; Särdragsextraktion; Generation Z;

    Abstract : As e-commerce and online shopping have increased worldwide, the interest and research of intelligent fashion systems have expanded. Given the competitive nature of the fashion market business, digital marketplaces depend on determining customer preferences. READ MORE

  6. 5. Incorporating Reinforcement Learning into Supervised Sequential Recommender Models

    University essay from Linköpings universitet/Statistik och maskininlärning

    Author : Patrick Siegfried Hiemsch; [2023]
    Keywords : ;

    Abstract : In the context of the significant expansion of e-commerce, Recommender Systems have become important tools for businesses, enhancing customer engagement through the personalization of product recommendations. This thesis investigates the integration of Reinforcement Learning concepts  into Supervised Learning frameworks, aiming to foster more accurate, novel and diverse recommendations. READ MORE